Active noise control method and system

Abstract

A method for reducing the power of an acoustic primary noise signal (dm(n)) at one or more control positions in a vehicle passenger compartment using an adaptive filter. The method comprising to compare a mean correlation coefficient (γm(n)) between an electrical error signal (em(n) and a modelled secondary anti-noise signal ŷm(n) with at least one predefined threshold (α, β).

The invention claimed is: 
     
       1. A method for reducing the power of an acoustic primary noise signal (d m (n), m=1, 2, 3, . . . ) at one or more control positions in a vehicle passenger compartment, the acoustic primary noise signal originating from an acoustic noise signal transmitted from a noise source through a respective primary sound path (P m , m=1, 2, 3, . . . ) to the respective control position, the method comprising:
 arranging an adaptive filter to receive input signals comprising:
 an electrical reference signal (x(n)) representing the acoustic noise signal, and 
 at least one electrical error signal (e m (n), m=1, 2, 3, . . . ) representing a respective acoustic signal detected by a respective sound sensor at the respective control position, 
 
 arranging the adaptive filter to provide and transmit at least one electrical control signal (y′ k (n), k=1, 2, 3, . . . ) to at least one acoustic transducer arranged in the compartment, 
 arranging the at least one acoustic transducer to, as a response to the at least one electrical control signal (y′ k (n), k=1, 2, 3, . . . ), provide and transmit a respective anti-noise signal through a respective secondary sound path (S km , k=1, 2, 3, . . . , m=1, 2, 3, . . . ) between the at least one acoustic transducer and the respective control position, arriving at the at least one control position as a respective acoustic secondary anti-noise signal (y m (n), m=1, 2, 3, . . . ), such as to minimize the respective electrical error signal (e m (n), m=1, 2, 3, . . . ), 
 providing a respective modelled secondary anti-noise signal (ŷ m (n), m=1, 2, 3, . . . ) from a respective secondary sound path model (Ŝ km , k=1, 2, 3, . . . , m=1, 2, 3, . . . ) 
 calculating a respective mean correlation coefficient (γ m (n), m=1, 2, 3, . . . ) between the respective electrical error signal (e m (n), m=1, 2, 3, . . . ) and the respective modelled secondary anti-noise signal (ŷ m (n), m=1, 2, 3, . . . ), and
 comparing at least one of the mean correlation coefficients (γ m (n), m=1, 2, 3, . . . ) with at least one predefined threshold (α, β), or 
 comparing an average value (γ(n)) of the at least one correlation coefficient (γ m (n), m=1, 2, 3, . . . ) with at least one predefined threshold (α, β). 
 
 
     
     
       2. The method of  claim 1 , wherein providing a modelled secondary anti-noise signal (ŷ(n)) comprises passing an electrical reference signal (x(n)) consecutively through a secondary sound path model (Ŝ) and then through the digital filter (W) of the adaptive filter. 
     
     
       3. The method of  claim 1 , wherein providing a modelled secondary anti-noise signal (ŷ(n)) comprises passing an electrical reference signal (x(n)) consecutively through the digital filter (W) of the adaptive filter and then through a secondary sound path model (Ŝ). 
     
     
       4. The method of  claim 1 , wherein a mean correlation coefficient (γ(n)) at a current time step is calculated as a function of a correlation coefficient (r(n)) at the current time step and a mean correlation coefficient at a previous time step (γ(n−1)), wherein a correlation coefficient (r(n)) is calculated from the N last samples of an error signal (e(n)) and a modelled secondary anti-noise signal (ŷ(n)), wherein the number of samples N is in the range of 100-10000, preferably 500-5000. 
     
     
       5. The method of  claim 1 , wherein if an amplitude of at least one mean correlation coefficient (γ m (n), m=1, 2, 3, . . . ) or an amplitude of the average value (γ(n)) of the at least one mean correlation coefficient (γ m (n), m=1, 2, 3, . . . ) is smaller than a first threshold value α, this is indicative of an optimally performing method, wherein the first threshold value α is in the range of 0.01-0.3, preferably 0.05-0.2. 
     
     
       6. The method of  claim 5 , wherein vehicle operative conditions and method parameters are registered in a database when the method is performing optimally. 
     
     
       7. The method of  claim 1 , wherein if at least one of the mean correlation coefficients (γ m (n), m=1, 2, 3, . . . ) or the average value (γ(n)) of the at least one mean correlation coefficient (γ m (n), m=1, 2, 3, . . . ) is larger than or equal to a second threshold value β, this is indicative of a diverging method, wherein the second threshold value β is in the range of 0.4-0.9, preferably 0.5-0.8. 
     
     
       8. The method of  claim 7 , further comprising changing one or more filter parameters chosen from step size (μ), sign of step size (μ), phase of step size (μ) and leakage factor. 
     
     
       9. The method of  claim 8 , wherein at least one of the step size (μ) and leakage factor is changed by multiplication with a correction factor negatively dependent on the amplitude of the mean correlation coefficient. 
     
     
       10. The method of  claim 8 , wherein a recovery rate of at least one of a modified step size (μ) and leakage factor is limited to a predefined value. 
     
     
       11. The method of  claim 7 , further comprising changing a secondary sound path model (Ŝ km , k=1, 2, 3, . . . , m=1, 2, 3, . . . ) used in the method to a secondary sound path model selected from a set of pre-measured secondary sound path models. 
     
     
       12. The method of  claim 7 , wherein when two or more sound sensors are used in the method, the method further comprises changing a spatial distribution of acoustic transducers and/or sound sensors in the compartment by switching on or off one or more acoustic transducers and/or sound sensors. 
     
     
       13. The method of  claim 7 , further comprising a step of stopping the method. 
     
     
       14. The method of  claim 1 , wherein if at least one of an amplitude of the mean correlation coefficients (γ m (n), m=1, 2, 3, . . . ) or an amplitude of the average value (γ(n)) of the at least one mean correlation coefficient (γ m (n), m=1, 2, 3, . . . ) is larger than or equal to a second threshold value β, this is indicative of a diverging method, wherein the second threshold value β is in the range of 0.4-0.9, preferably 0.5-0.8. 
     
     
       15. The method of  claim 1 , wherein if an amplitude of the at least one mean correlation coefficient (γ m (n), m=1, 2, 3, . . . ) or an amplitude of the average value (γ(n)) of the at least one mean correlation coefficient (γ m (n), m=1, 2, 3, . . . ) is larger than or equal to a first threshold value α and at least one of the mean correlation coefficients (γ m (n), m=1, 2, 3, . . . ) or the average value (γ(n)) of the at least one mean correlation coefficient (γ m (n), m=1, 2, 3, . . . ) is smaller than a second threshold value β, this is indicative of a non-optimally performing method, wherein the first threshold value α is in the range of 0.01-0.3, preferably 0.05-0.2, and the second threshold value β is in the range of 0.4-0.9, preferably 0.5-0.8. 
     
     
       16. The method of  claim 1 , wherein if an amplitude of the at least one mean correlation coefficient (γ m (n), m=1, 2, 3, . . . ) or an amplitude of the average value (γ(n)) of the at least one mean correlation coefficient (γ m (n), m=1, 2, 3, . . . ) is larger than or equal to a first threshold value α and at least one of an amplitude of the mean correlation coefficients (γ m (n), m=1, 2, 3, . . . ) or an amplitude of the average value (γ(n)) of the at least one mean correlation coefficient (y m (n), m=1, 2, 3,...) is smaller than a second threshold value β, this is indicative of a non-optimally performing method, wherein the first threshold value α is in the range of 0.01-0.3, preferably 0.05-0.2, and the second threshold value β is in the range of 0.4-0.9, preferably 0.5-0.8. 
     
     
       17. The method of  claim 1 , wherein the adaptive filter is a filter selected from a group consisting of filtered-x-LMS, leaky filtered-x-LMS, filtered-error-LMS and modified-filtered-x-LMS. 
     
     
       18. An active noise control system for reducing the power of an acoustic primary noise signal (d m (n), m=1, 2, 3, . . . ) at one or more control positions in a vehicle passenger compartment, the acoustic primary noise signal originating from an acoustic noise signal transmitted from a noise source through a respective primary sound path (P m , m=1, 2, 3, . . . ) to the respective control position, wherein the system comprises:
 an adaptive filter, which is arranged to take as input signals
 an electrical reference signal (x(n)) representing the acoustic noise signal, and 
 at least one electrical error signal (e m (n), m=1, 2, 3, . . . ) representing a respective acoustic signal detected by a respective sound sensor at the respective control position, 
 
 and which adaptive filter is arranged to provide and transmit at least one electrical control signal (y′ k (n), k=1, 2, 3, . . . ) to at least one acoustic transducer arranged in the compartment, which at least one acoustic transducer in response to the at least one electrical control signal (e m (n), m=1, 2, 3, . . . ) is arranged to provide and transmit a respective acoustic anti-noise signal through a respective secondary sound path (S km , k=1, 2, 3, . . . , m=1, 2, 3, . . . ) between the at least one acoustic transducer and the respective control position, arriving at the at least one control position as a respective acoustic secondary anti-noise signal (y m (n), m=1, 2, 3, . . . ), such as to minimize the respective electrical error signal (e m (n), m=1, 2, 3, . . . ), 
 
       wherein the system further comprises
 a performance monitoring unit arranged to:
 provide a respective modelled secondary anti-noise signal (ŷ m (n), m=1, 2, 3, . . . ) from a respective secondary sound path model (Ŝ km , k=1, 2, 3, . . . , m=1, 2, 3, . . . ), 
 calculate a respective mean correlation coefficient (γ m (n), m=1, 2, 3, . . . ) between the respective electrical error signal (e m (n), m=1, 2, 3, . . . ) and the respective modelled secondary anti-noise signal (ŷ m (n), m=1, 2, 3, . . . ), and to
 compare at least one of the mean correlation coefficients (γ m (n), m=1, 2, 3, . . . ) with at least one predefined threshold (α, β), or 
 compare an average value (γ(n)) of the at least one correlation coefficient (γ m (n), m=1, 2, 3, . . . ) with at least one predefined threshold (α, β).